Battery Performance Prediction Using Fused Stage Parameters
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Solution Overview
Problem
Current battery production processes require inefficient and costly electrochemical processes for battery capacity grading, leading to low efficiency and high costs in determining battery performance.
Innovation Solution
A battery performance prediction method using artificial intelligence, employing neural networks to fuse first and second stage parameters without necessitating the second stage process, thereby improving prediction accuracy and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electrochemical processes (discharging and charging) are performed on batteries to determine actual capacity, then battery performance can be accurately evaluated, but efficiency is low and costs are high
Solution Approach 1:
The patent uses neural network models to create a virtual copy of the electrochemical testing process. Instead of physically discharging and charging batteries to measure capacity, the trained model predicts battery performance parameters based on production data, replacing the physical measurement process with a computational simulation that yields similar results without the time and cost overhead
Solution Approach 2:
The patent performs preliminary training of neural network models using historical battery data before actual performance determination is needed. The model learns from past electrochemical test results and production parameters, so when new batteries need evaluation, the predictions can be made immediately without performing the actual discharge/charge cycles, effectively preparing the prediction system in advance
2Measurement precision
If electrochemical processes (discharging and charging) are performed on batteries to determine actual capacity, then battery performance can be accurately evaluated, but costs are high
Solution Approach 1:
The patent replaces expensive physical electrochemical testing with a computational model that replicates the measurement function. The neural network predicts battery capacity and performance parameters by processing production data through learned relationships, eliminating the need for costly discharge/charge testing while maintaining acceptable accuracy for production purposes
Solution Approach 2:
The patent uses inexpensive computational resources (neural network predictions) instead of expensive physical resources (electrochemical test equipment, time, and energy). The model provides a low-cost alternative that consumes minimal energy compared to actual battery cycling tests, making performance determination economically viable at scale
3Measurement precision
If second stage process is performed on battery to obtain second stage parameter, then prediction accuracy of battery performance can be improved, but efficiency of determining battery performance decreases
Solution Approach 1:
The patent divides the battery performance prediction into two sequential neural network stages: the first network predicts intermediate parameters from production data, and the second network uses those predictions along with production parameters to predict final performance. This segmentation allows the system to capture complex relationships in steps rather than requiring all data from a time-consuming second stage electrochemical process
Solution Approach 2:
The first neural network performs preliminary prediction of intermediate parameters that would otherwise require actual electrochemical testing to obtain. By predicting these intermediate values computationally before final performance prediction, the system captures important relationship patterns without performing the physical second-stage testing that would consume time and resources
Data Source
AI summary
A battery performance prediction method is provided. In the method, the first network first predicts a parameter of a battery in a procedure of a second stage process based on a parameter of the battery in a procedure of a first stage process, and then fuses the parameter of the battery in the procedure of the first stage process with the parameter that is obtained through prediction and that is of the battery in the procedure of the second stage process, and a second network predicts a performance parameter of the battery based on a plurality of features of the battery, thereby effectively improving prediction accuracy of the performance parameter of the battery.